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Moralis MCP Server

Moralis MCP Server

Official
by MoralisWeb3
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

solana_getspl

Retrieve token balances for any Solana address, filtering out spam tokens as needed, to monitor cryptocurrency holdings across mainnet or devnet networks.

Instructions

Gets token balances owned by the given address

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "network": "<network>",
      "address": "<address>"
    },
    "name": "solana_getspl"
  }
}

Input Schema

NameRequiredDescriptionDefault
networkYesThe network to query
addressYesThe address to query
excludeSpamNoShould exclude spam tokens

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states a read operation ('Gets'), implying it's likely non-destructive, but doesn't disclose behavioral traits like rate limits, authentication needs, error conditions, or what the return format looks like (e.g., list of balances with details). This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded and appropriately sized, with every word contributing to clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (a read operation with 3 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on return values, error handling, or behavioral constraints, making it inadequate for the agent to fully understand how to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters (network, address, excludeSpam). The description adds no additional meaning beyond implying the address parameter is for querying balances, which is already clear from the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Gets') and resource ('token balances owned by the given address'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'solana_balance' or 'solana_getportfolio', which might also retrieve balance-related information, so it misses the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'solana_balance', 'solana_getportfolio', 'solana_gettokenholders'), there's no indication of context, prerequisites, or exclusions, leaving the agent to infer usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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